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Polymarket Fill Risk

polymarket_fill_risk
Read-onlyIdempotent

Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of market (single-market mode) or event (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideNoSingle-market: buy_yes | sell_yes | buy_no | sell_no (default buy_yes). Basket: sell_yes | buy_yes (default auto — sell if partition sum > 1, buy if < 1).
eventNoBasket mode: event slug or full polymarket.com URL — checks every leg of the partition.
marketNoSingle-market mode: market slug or full polymarket.com URL.
size_usdNoSingle-market: USD to spend (buys) or target proceeds (sells). Basket: settlement notional — shares per leg, each paying $1 at resolution. Default 1000, clamp 10–1,000,000.

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds substantial behavioral context beyond annotations. It details what the tool does (walks ladder, returns fields like vwap_fill_price, slippage_pp, verdict) and warns about partial basket fills and forced directional risk. Annotations already indicate readOnlyHint=true, which aligns with description's 'check' nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with bold headers for modes and front-loaded with the core function. However, it is somewhat verbose in places (e.g., lengthy explanation of basket fill risks). Still, every sentence adds value, earning a high score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully explains all return fields for both modes (top_of_book, vwap_fill_price, etc.). It also covers edge cases (thin legs, forced directional risk) and rationale. This is exceptionally complete for a complex dual-mode tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds significant meaning: explains mutual exclusivity of market/event, side defaults and auto-detection for baskets, and how size_usd is interpreted differently for single vs. basket modes (max spend vs. settlement notional). This goes well beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: it checks realizable vs. theoretical edge against live CLOB order-book depth. It explicitly distinguishes between single-market and basket modes, and differentiates itself from sibling tools like polymarket_arbitrage and polymarket_edges by specifying it is a pre-trade risk check.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (partial fills convert arb to unhedged directional position), offering clear context and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation5/5

Each tool has a highly distinct purpose, from Disney character lookups to financial data and prediction market analysis. Agent can easily distinguish them by name and description.

Naming Consistency2/5

Naming conventions vary wildly: some use verb_noun (list_characters), others are phrases (ask_pipeworx) or compound nouns (polymarket_arbitrage). No consistent pattern across tools.

Tool Count1/5

34 tools is far too many for a Disney-themed server. Only 3 tools (list_characters, search_characters, get_character) relate to Disney; the rest are general-purpose data tools that belong elsewhere.

Completeness1/5

For a Disney server, the tool surface is severely incomplete. Missing basic CRUD for characters, no info on movies, parks, or media. Extraneous tools do not fill these gaps.